Work

Improving the Signal‐to‐Noise ratio in genome‐wide association studies

Year: 2009

Type: article

Abstract: Genome-wide association studies employ hundreds of thousands of statistical tests to determine which regions of the genome may likely harbor disease-causing alleles. Such large-scale testing simultaneously requires stringent control over type I error and maintenance of sufficient power to detect true associations. These contradictory goals have led some researchers beyond Bonferroni correction of P-values to an exploration of methods to improve the detection of a few true effects in the presence of many unassociated loci. This article reviews how Genetic Analysis Workshop 16 Group 5 investigators proposed to adjust for multiple tests while simultaneously using information about the structure of the genome to improve the detection of true positives.

Source: Genetic Epidemiology

DOI: 10.1002/gepi.20469

Authors: Lisa J. Martin, Guimin Gao, Guolian Kang, Yixin Fang, Jessica G. Woo

Institutions: Cincinnati Children's Hospital Medical Center, University of Cincinnati, University of Alabama at Birmingham, Georgia State University

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute of General Medical Sciences